Correlation Congruence for Knowledge Distillation

Baoyun Peng, Xiao Jin, Jiaheng Liu, Dongsheng Li, Yichao Wu, Yu Liu, Shunfeng Zhou, Zhaoning Zhang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 5007-5016

Abstract


Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level. However, they usually ignore the correlation between multiple instances, which is also valuable for knowledge transfer. In this work, we propose a new framework named correlation congruence for knowledge distillation (CCKD), which transfers not only the instance-level information but also the correlation between instances. Furthermore, a generalized kernel method based on Taylor series expansion is proposed to better capture the correlation between instances. Empirical experiments and ablation studies on image classification tasks (including CIFAR-100, ImageNet-1K) and metric learning tasks (including ReID and Face Recognition) show that the proposed CCKD substantially outperforms the original KD and other SOTA KD-based methods. The CCKD can be easily deployed in the majority of the teacher-student framework such as KD and hint-based learning methods.

Related Material


[pdf]
[bibtex]
@InProceedings{Peng_2019_ICCV,
author = {Peng, Baoyun and Jin, Xiao and Liu, Jiaheng and Li, Dongsheng and Wu, Yichao and Liu, Yu and Zhou, Shunfeng and Zhang, Zhaoning},
title = {Correlation Congruence for Knowledge Distillation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}